基于生成对抗和双重语义感知的配电网量测数据缺失重构

杨玉莲; 齐林海; 王红; 苏林萍; 徐永海

doi:10.7500/AEPS20190605007

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基于生成对抗和双重语义感知的配电网量测数据缺失重构

同步相量测量技术在配电网中的应用 | 更新时间：2025-06-16

- 基于生成对抗和双重语义感知的配电网量测数据缺失重构
- Reconstruction of Missing Measurement Data in Distribution Network Based on Generative Adversarial Network and Double Semantic Perception
- 电力系统自动化-方正平台 2020年44卷第18期页码：46-54
- 作者机构：
  
  1.华北电力大学控制与计算机工程学院，北京市 102206
  2.华北电力大学电气与电子工程学院，北京市 102206
- 作者简介：
  
  杨玉莲(1995—)，女，硕士研究生，主要研究方向：深度学习、电力大数据处理。E-mail：1435504828@qq.com
  齐林海(1964—)，男，通信作者，博士，副教授，主要研究方向：电能质量智能信息处理、智能电网大数据应用。E-mail：qilinhai@ncepu.edu.cn
  王红(1978—)，女，博士，讲师，主要研究方向：大数据应用技术、电能质量智能信息处理。E-mail：wh@ncepu.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(51277069);国家电网公司科技项目(52094018001C)
- DOI：10.7500/AEPS20190605007
  中图分类号：
- 收稿日期：2019-06-05，
  
  修回日期：2019-09-17，
  
  网络出版日期：2020-02-16，
  
  纸质出版日期：2020-09-25
- 稿件说明：
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杨玉莲,齐林海,王红等.基于生成对抗和双重语义感知的配电网量测数据缺失重构[J].电力系统自动化,2020,44(18):46-54.

YANG Yulian,QI Linhai,WANG Hong,et al.Reconstruction of Missing Measurement Data in Distribution Network Based on Generative Adversarial Network and Double Semantic Perception[J].Automation of Electric Power Systems,2020,44(18):46-54.
杨玉莲,齐林海,王红等.基于生成对抗和双重语义感知的配电网量测数据缺失重构[J].电力系统自动化,2020,44(18):46-54. DOI： 10.7500/AEPS20190605007.

YANG Yulian,QI Linhai,WANG Hong,et al.Reconstruction of Missing Measurement Data in Distribution Network Based on Generative Adversarial Network and Double Semantic Perception[J].Automation of Electric Power Systems,2020,44(18):46-54. DOI： 10.7500/AEPS20190605007.

摘要

传统的数据缺失重构技术大多依赖数理统计方法和先验知识结合机理分析构建数学模型，但是配电网量测数据具有高维、时变、非线性特征，复杂度高、表征难度大，难以保证高精度重构。文中提出一种利用无监督生成对抗训练方式自主提取数据特征并结合双重语义感知重构约束实现数据缺失重构的方法。其中，基于二维卷积的重构模型和量测数据二维灰度图像化训练增强了模型泛化能力和稳定性。该方法无需先验知识的分布假设与显式物理建模，在保证数据特征提取最大化的同时，有效提高了重构数据的精确性。最后，利用实测数据验证了该方法在重构缺失数据上的有效性。

Abstract

Traditional data missing reconstruction technology mostly relies on mathematical statistics method and prior knowledge combined with mechanism analysis to construct mathematical models. However

measurement data in distribution network has high dimensional

time-variant

non-linear characteristics with high complexity and difficult characterization

and it is difficult to ensure high-precision reconstruction. In this paper

an unsupervised generation antagonism training method is proposed to extract data features autonomously and reconstruct missing data with reconstruction constraints of dual semantic perception. In the method

the reconstruction model based on two-dimensional convolution and the two-dimensional gray image training of measurement data enhance the generalization ability and stability of the model. This method does not need the distribution hypothesis of prior knowledge and explicit physical modeling

and it effectively improves the accuracy of reconstructed data while guaranteeing maximum feature extraction. Finally

the validity of this method in reconstructing missing data is verified by the field measured data.

关键词

Keywords

references

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